Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Project description:A previously described low-fitness, high stress-resistant, variant of Listeria monocytogenes LO28 WT was subjected to an experimental evolution regime, selecting (in two parallel lines) for increased fitness in unstressed conditions. Evolved variants with increased fitness reverted to WT-like stress resistance. Whole genome sequencing and proteomics were used to identify differences between the ancestral and evolved strains.
Project description:Genome-wide association studies have identified numerous genetic variants conferring autoimmune disease risk. Most of these genetic variants lie outside protein-coding genes hampering mechanistic explorations. Numerous mRNAs are also differentially expressed in autoimmune disease but their regulation is also unclear. The majority of the human genome is transcribed yet its biologic significance is incompletely understood. We performed whole genome RNA-sequencing [RNA-seq] to categorize expression of mRNAs, known and novel long non-coding RNAs [lncRNAs] in leukocytes from subjects with autoimmune disease and identified annotated and novel lncRNAs differentially expressed across multiple disorders. We found that loci transcribing novel lncRNAs were not randomly distributed across the genome but co-localized with leukocyte transcriptional enhancers, especially super-enhancers, and near genetic variants associated with autoimmune disease risk. We propose that alterations in enhancer function, including lncRNA expression, produced by genetics and environment, change cellular phenotypes contributing to disease risk and pathogenesis and represent attractive therapeutic targets.
Project description:We used a Drosophila melanogaster line (a "double balancer") carrying balancer chromosomes for both the second (CyO) and third (TM3) chromosomes. We crossed the double balancer to an isogenic wild-type "virginizer" line to obtain trans-heterozygous adults from the F1 generation. Whole-genome sequencing and mate pair sequencing were used to identify Single Nucleotide Variants (SNVs) and Structural Variants (SVs) on both chromosomes.
Project description:<p>We investigated the cumulative contribution of rare, exonic genetic variants on the concentration of 1,487 metabolites and 53,714 metabolite ratios in urine by performing gene-based tests based on 226,233 variants from up to 4,864 participants of the German Chronic Kidney Disease (GCKD) study. There were 128 significant associations (53 metabolite-gene and 75 metabolite ratio-gene pairs) involving 30 unique genes, 16 of which are known to underlie recessively inherited inborn errors of metabolism (IEMs). Across the 30 genes, 47% of individuals carried at least one rare missense, stop or splice variant. The 30 genes were strongly enriched for shared high expression in liver and kidney (OR=65, p-FDR=3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of whole-exome sequencing data in the UK Biobank allowed for linking genes to diseases that could plausibly be explained by the identified metabolites. In silico constraint-based modeling of knockouts of the implicated genes in a virtual whole-body, organ-resolved metabolic human correctly predicted the observed direction of metabolite changes in urine and blood, highlighting the potential of linking population genetics to modeling to validate associations and to predict metabolic consequences of yet unknown IEMs. Our study extends the map of genes influencing urine metabolite concentrations, reveals metabolic processes and connected health outcomes, and implicates novel candidate variants and genes for IEMs.</p><p><br></p><p>Further links;</p><p><a href='https://www.gckd.org/' rel='noopener noreferrer' target='_blank'>German Chronic Kidney Disease (GCKD)</a></p>
2021-03-09 | MTBLS284 | MetaboLights
Project description:deCODE Release 2: whole-genome de novo variants from the proband from 1548 Icelandic trios
